Mining Non-redundant Association Rules based on Concise Bases

Xu, Yue & Li, Yuefeng (2007) Mining Non-redundant Association Rules based on Concise Bases. International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI), 21(4), pp. 659-675.

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Association rule mining has made many achievements in the area of knowledge discovery. However, the quality of the extracted association rules has not drawn adequate attention from researchers in data mining community. One big concern with the quality of association rule mining is the size of the extracted rule set. As a matter of fact, very often tens of thousands of association rules are extracted among which many are redundant thus useless. In this paper, we first analyze the redundancy problem in association rules and then propose a reliable exact association rule basis from which more concise non-redundant rules can be extracted. We prove that the redundancy eliminated using the proposed reliable association rule basis does not reduce the belief to the extracted rules. Moreover, this paper propose a level wise approach for efficiently extracting closed itemsets and minimal generators which is also a key issue in closure based association rule mining.

Impact and interest:

11 citations in Scopus
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6 citations in Web of Science®

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ID Code: 13716
Item Type: Journal Article
Refereed: Yes
Additional Information: For more information, please refer to the journal's website (see hypertext link) or contact the author.
DOI: 10.1142/S0218001407005600
ISSN: 1793-6381
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2007 World Scientific Publishing
Deposited On: 10 Jun 2008 00:00
Last Modified: 29 Feb 2012 13:34

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